Name: Cryptocurrency-predicting RNN Model - Deep Learning w/ Python, TensorFlow and Keras p.11
Uploaded: 2020-03-31T18:12:11.000Z
Duration: 23 min 6 s
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Everybody and welcome to yet another deep learning with python tensorflow and Kare aas tutorial.

Sentences with advanced comparative

In this tutorial, we're gonna continue with last tutorial where we're attempting to predict future price movements of a certain crypto currency based on the sequence in the historical prices and volume of that Cryptocurrency as well as other major crypto currencies.

And we're trying to do this with the recurrent neural network.

The next thing that we want to do here is we're gonna import times.

We're gonna use that just in a moment and we need Thio create a few more Constance here.

The first thing we're gonna do is iPAQ So how many blocks we want to train this model for?

Then we're gonna go with a batch size and we're gonna go with 64 to start weaken.

And then finally, we're gonna go with a name on.

We're gonna make this an F string, and what you want is a name that is descriptive of the model because generally you're gonna tinker a little bit with the model, tweak it a little bit here and there re run it again, and then you're gonna do the same thing again.

Anyway, um, so you want to have a unique name, both for the model that you save as well as intense or bored.

So later you can compare the results of a bunch of different models and you don't have to be like I don't know what model that was so or worse, you overwrite the other models.

So I'm gonna go with sequence, length, dash sequence, dash, future period.

And then we'll just throw in and int time that time here.

That'll give us a nice, unique model name.

So now we're gonna do is import all the tents airflow stuff that we need.

So import tensor flow as t f as t f Stop it sublime from tensor float.

Kare aas dot models were gonna import in poor sequence show.

Uh ah, You're such a It's a Quintus D'oh!

Oh, this This might be a hard one to get through from 10 serve tens or float.

Kare aas dot layers were going to import dense dropout.

L s t m probably also coud cou d n n l s t Do you know I had it right.

L s t m and then batch batch normalization.

Um, all of these we've seen, except for I don't remember if we've done batch normalization.

Basically, just normalization, but between the layers.

So for the same reason you want to normalize your input data batch normalization can be useful from layer to layer because because really, you can think of each layer.

If it's going to another layer, that layer, its output is kind of like your it's it's a new input for a new layer.

So you would want it if you could normalize that data.

So we're gonna do batch normalization there.

Okay, Once we've got that more imports from tensor float up Keira stock callbacks.

Uh, anyway, tensor board and model checkpoint, I think be intense or board probably needs to be capitalized.

We've really already seen this callback model checkpoint is a fancy dancy little call back where basically, you can set various parameters as to when you want to save certain checkpoints.

A validation loss would be another good one.

Basically, each time it reaches a new max, you could have you could have the model actually save that checkpoint That way, you know, you can run for 100 epoxy now, private the end of 100 box before you got there.

The model over fit, and it kind of went crappy.

Um, and you could save every single iPAQ, but this might wait space or whatever.

Um, anyway, this is makes a nice way to always save the best one.

Okay, Uh, once we have that, we're gonna scroll on down.

So make some space here, Uh, and we're just gonna There's a lot of this coded stuff we've seen before.

We've seen that one model, that ad, and we're gonna add, uh, I'm gonna use Cody and else tm If you're on CPU version in tensorflow, use a regular l S T M.

It's gonna be 1 28 nodes in this layer and input shape and something bangs on this desk, and it makes me mad that what is that noise I could never find What that noise is striking drives me nuts.

And then sometimes it's, like, stops happening for, like, months.

Input shape trained ex, uh, train exact shape, one cold.

So we're inside the cootie in an L s t m Still.

So then return underscore er sequences equals true.

So you should already know that what the next layer is gonna be.

So then model that ad and we will throw in a drop outs, and we're gonna d'oh 0.2.

So model that ad batch normalization and no parameters there.

And then I am going to copy this and then paste it twice paste and paste, and then we're gonna have a dense layer.

So because we're gonna have a dense layer, I'm gonna remove the return sequences.

And so now it's a 1 28 to another 1 28 and then to a final 1 28 Now, for whatever reason, in my notes, I have this as a dropout of 0.1.

But I'm pretty confident your 0.2 would probably work.

Simple one, Uh, and I think we'll leave everything else as the same.

Now we're gonna add eight dense layer, but not the output layer.

Just a dense layer model That ad, Yes, that one dense 32 activation will be rectified Linear.

Which reminds me, if you can't use Cudi, an analyst him make sure you throw in some activations.

I would do either tan H because that's what Cody N N L C M's using, or you could just use rectified linear.